Presenting supplemental content in context

ABSTRACT

Techniques that may be used for detecting a primary content (e.g., a web page) that the user is viewing and presenting one or more pieces of supplemental content (e.g., social media data) together with the primary content. The supplemental content presented to the user together with the primary content may be content that is matched to the primary content and therefore detected to be relevant to the user. Detection of primary content and matching to supplemental content may be carried out based on a comparison of entities related to the primary and supplemental content. In some embodiments, an analysis of the primary content for entities may include ordering entities according to significance in the primary content and selecting top entities for comparison. Also, in some embodiments, multiple pieces of supplemental content may be displayed to a user categorized based on entity.

RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.13/602,015, filed on Aug. 31, 2012, and entitled “PRESENTINGSUPPLEMENTAL CONTENT IN CONTEXT”, which is a Continuation-in-Part ofU.S. patent application Ser. No. 12/795,392, filed on Jun. 7, 2010,entitled “PRESENTING SUPPLEMENTAL CONTENT IN CONTEXT”. The entireties ofthese applications are incorporated herein by reference.

This application is related to U.S. application Ser. No. 12/270,974,entitled “Social network referral coupons,” filed on Nov. 14, 2008,which is herein incorporated by reference in its entirety.

This application is related to U.S. application Ser. No. 12/614,457,entitled “Social browsing,” filed on Nov. 9, 2009, which is hereinincorporated by reference in its entirety.

This application is related to U.S. application Ser. No. 12/633,798,entitled “Generating activities based upon social data,” filed on Dec.9, 2009, which is herein incorporated by reference in its entirety.

This application is related to U.S. application Ser. No. 12/761,666,entitled “Social home page,” filed on Apr. 16, 2010, which is hereinincorporated by reference in its entirety.

BACKGROUND

Network services—including messaging systems, blogging services,discussion boards, and social network services—permit users to identifyone another and share multiple types of information with one another.Users of these services may establish profiles that include informationabout the users' identity and interests, and other users may view thisinformation or receive notifications when the information changes. Usersmay also send messages to one another, such as by sending privatemessages, sending multicast messages, and sending broadcast messages.Some network services enable users to share information about theirpast, present, or future interests and activities, and allow other usersto receive notifications about these interests and activities. Further,pictures, audio, videos, web content, and other types of information maybe shared between users. Sending such messages and sharing suchinformation between users is done actively, by a user designating otherusers to receive some information, or passively, such as by postingmessages and information in a publicly-accessible location or in alocation accessible to other users with which the user has a connection.

To receive such information, users of network services establish linksor connections to one another that indicate an offline relationshipbetween the users. For example, a connection may be created betweenfamily members, friends, or co-workers. Additionally, a connection maybe created between people without an existing offline relationship, suchas between a user and another that the user has identified in thenetwork service. When users are connected in this way, the networkservice may make available to the users information that has beenactively or passively shared between users (e.g., explicitly broadcastor posted in an accessible location).

Network services such as these include services like Facebook®,Twitter®, Microsoft® Mingle, and Google® Reader, which permit users toshare information with “friends” (in Facebook®) and “followers” (inTwitter® and Google® Reader).

Other ways exist in which people may share information and exchangecontent. For example, people may publish information they release online(e.g., blog entries, photos, etc.) or distribute notifications regardingthe information via a feed protocol like the Really Simple Syndication(RSS) protocol or the Atom Publishing Protocol (Atom). Other people maythen download and view entries in the feed using a feed reader.

Some services exist that manipulate information available via networkservices, feed readers, and other web distribution tools. These toolsoperate to filter or reorganize the available information based onexplicit user input. For example, the TwitterTim.es service allows auser to establish particular “channels” from his or her Twitter feedthat each filter the Twitter content that fits the parameters of thosechannels.

SUMMARY

Individual users can become overwhelmed with the amount of content thatis available for consumption via network services, feeds, and othersources. Users often find that viewing all of the content isunachievable. When this happens, users may then avoid viewing anycontent at all or may view some content without being sure that they areviewing the most relevant content.

Described herein are techniques that may be used for detecting a primarycontent (e.g., a web page) that the user is viewing and presenting oneor more pieces of supplemental content (e.g., social media data)together with the primary content. The primary content may be anycontent that the user is experiencing, and the supplemental content maybe any distributed information to which the user has subscribed and/orin which the user has previously showed interest. The supplementalcontent presented to the user together with the primary content may becontent that is matched and related to the primary content and thereforedetected to be relevant to the user. Detection of primary content andmatching to supplemental content may be carried out based on acomparison of entities related to (e.g., mentioned in) the primary andsupplemental content. In some embodiments, an analysis of the primarycontent for entities may include ordering entities according tosignificance in the primary content and selecting top entities forcomparison. Also, in some embodiments, multiple pieces of supplementalcontent may be displayed to a user categorized based on entity.

In one illustrative embodiment, a user may be viewing a web page asprimary content. Various entities referenced in the web page (e.g.,words used in the web page) may be detected from an analysis of the webpage. The entities may then be ordered according to their significancein the web page. Entities may also be extracted from social media data,and a comparison of entities for the web page and entities for thesocial media data carried out. A set of matched entities, and socialmedia related to those entities, may then be presented to the user as aset of social media data that is relevant to the user at that time(i.e., related to the primary content the user is viewing). In this way,a user may be aided in locating and viewing social media data that isrelevant to the user.

The foregoing is a non-limiting summary of the invention, which isdefined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a flowchart of one exemplary overall process for presentingsupplemental content to a user;

FIG. 2 is a flowchart of one exemplary process for analyzing primarycontent to determine entities related to the primary content;

FIGS. 3A and 3B are each flowcharts of exemplary techniques for orderingentities related to primary content according to a significance of theentities;

FIG. 4 is a flowchart of one exemplary process for analyzingsupplemental content to determine entities related to the supplementalcontent;

FIG. 5 is a flowchart of one exemplary process for obtainingsupplemental content for analysis;

FIG. 6 is a flowchart of one exemplary process for presentingsupplemental content to a user together with primary content;

FIGS. 7A and 7B are screenshots of exemplary user interfaces that may beused in some embodiments;

FIG. 8 is a block diagram of one exemplary computing device with whichsome embodiments may operate; and

FIG. 9 illustrates one exemplary computer system in which someembodiments may operate.

DETAILED DESCRIPTION

As the number and type of ways to distribute information grows, and asthe number of people distributing information grows, individual usersmay become overwhelmed with the amount of content that is directed tothem. A user, for example, may become overwhelmed by the amount ofinformation available via network services and other sources (e.g.,feeds, email accounts, instant messaging, etc.) and thus may not readall or even any of the information.

Users, however, initially become affiliated with and use these servicesso that the users could receive and view the content distributed viathese services. Users may therefore attempt to view the most recent ormost “important” information, but may still overlook some content. Somemay sort or filter the content, but sorting or filtering the contentbased on explicit input from the user (e.g., a keyword search) stillleaves open the possibility that the user's input is not comprehensiveand some important content will be missed by the user. The risk ofmissing important content may make some users feel anxious.

Users want to receive and view content distributed via these servicesbecause they expect that the content will be meaningful and relevant tothem. Users are therefore most likely to be interested in viewing“relevant” content. What content is “relevant,” however, may vary basedon current interests and activities of a user. Content that may berelevant to a user at one time may not be relevant to the user atanother time, based on a change in the user's interests and activities.Detecting a user's current interests, then, may aid the user inidentifying and viewing relevant content.

Described herein are various techniques for detecting a user's currentinterests and activities to determine topics that are relevant to theuser. Topics that may be relevant may be used to examine set ofavailable content (e.g., social media data) and “surface” content fromthe set that is relevant to the user at that time.

In some embodiments, to detect a user's current interests andactivities, primary content that the user is experiencing may beanalyzed to determine a topic of the primary content, which may beindicative of the user's current interests/activities. This analysis maybe carried out by identifying entities that are related to the primarycontent, including entities that are referenced in the primary content.In some cases, the entities that are identified may be ordered bysignificance to determine the most significant entities related to theprimary content, which may further identify the topic of the primarycontent and aid in identifying the user's current interests/activities.Entities related to each of multiple units of supplemental content mayalso be identified. The entities of the primary content and the entitiesof the supplement content may then be compared to determine matches inentities and, thereby, identify supplemental content that relates to thesame topic as the primary content and may be relevant to the user atthat time. Units of supplemental content related to entities in theprimary content may then be presented to the user for viewing, such thatthe user can view supplemental content that may be relevant to the userat that time.

For example, in accordance with some of the techniques described herein,a system may detect a user's interests based on a web page (one exampleof primary content) that the user is viewing by attempting to identifytopics to which the web page relates. To do so, the system may identifyentities referenced by the web page. Social media data and/or otherdistributed information to which the user subscribes (examples ofsupplemental content) may be similarly analyzed to identify entities towhich the social media data relates. Social media data that relates tothe same entities as the web page may be inferred to be related to thesame topic about which the user is reading and therefore inferred to berelevant to the user at that time. Social media data that relates tothose entities may therefore be presented to the user together with theweb page, to permit the user to view, reply to, or otherwise interactwith relevant social media data while viewing the web page.

Embodiments of the principles described herein are not limited tooperating with web pages and social media data. Rather, embodiments mayoperate in connection with any suitable primary content and supplementalcontent.

Primary content, as used herein, includes any electronic content thatthe user is presently or was recently experiencing (e.g., viewing,listening, interacting with, etc.). Primary content may include webpages, word processing documents, text documents, presentation slides,photographs, email messages, audio/visual content (e.g., music, movies,television programs, etc.), and/or any other type of electronic content.When a user is experiencing primary content, this primary content may beanalyzed to determine the user's current interest(s) and then used todetermine supplemental content (e.g., social media data) to be presentedto the user as relevant supplemental content.

Supplemental content, as used herein, includes any distributedinformation to which the user has subscribed. Distributed informationincludes any information available electronically. A user may subscribeto distributed information when the user has requested that thedistributed information be provided to the user. Requesting that thedistributed information be provided to the user may be done passively oractively. For example, the user may request that information be providedby transmitting a request to receive the distributed information;retrieving (or configuring a computer program or other agent toretrieve) distributed content from a source; establishing, within anetwork service, a connection to another user, such that informationdistributed by the other user (e.g., broadcast, multicast, or unicast)is routed to and received by the user; establishing a location at whichto receive distributed information; and/or any other actions to retrieveor receive distributed information.

Distributed information, as used herein, includes content to which auser has subscribed. Examples of distributed information include networkdata, which includes data distributed by and/or via a network serviceused by the user. Network data may include social media data (i.e.,network data distributed via a social network service), which mayinclude messages shared between users of a social network service(including public social network services and enterprise social networkservices), media shared between users (including photos, videos, andaudio/visual media) and metadata associated with the media (e.g., time,data, place, description, caption, people pictured, etc.), andidentifying information regarding users. Distributed informationregarding network data may include the network data itself ornotifications regarding availability of new or changed network data.Distributed information may additionally or alternatively include webcontent available via feeds (e.g., RSS or Atom feeds), such as blogentries, news articles, picture galleries, and other web content.Distributed information may additionally or alternatively includeinstant messaging information (e.g., messages or statuses) and emailmessages. A unit of distributed information (e.g., a unit of socialmedia data) may be any discrete piece of distributed information, suchas a discrete message, blog entry, email message, etc.

In some embodiments, primary content may be any suitable content that auser has chosen to experience, and supplemental content presented to theuser may be any other distributed information that is available to theuser for viewing and that a content selection system has detected may berelevant to the user at that time and has automatically presented to theuser via a sidebar display.

Entities, as used herein, are references to topics concerning primary orsupplemental content. When primary or supplemental content is textual,an entity may be a text word or phrase used in the primary orsupplemental content. When primary or supplemental content is audio, anentity may be a sound contained in the audio, such as a word or phrasespoken in the audio. When primary or supplemental content is visual, anentity may be a pictured object (e.g., a person, animal, etc.). Entitiesmay also be referenced in metadata of primary or supplemental content,such as a time or place at which primary or supplemental contentoriginated or a source of the primary or secondary content. Entities maybe determined using any suitable analysis of any part of primary orsupplemental content, examples of which are discussed below.

For ease of description and clarity, in various examples below primarycontent will be discussed as a web page, supplemental content will bediscussed as social media data, and entities will be described as textcontained in either the web page or the social media data. However,embodiments are not limited to operating with these types or any otherparticular types of primary content, supplemental content, and entities.Rather, embodiments may operate to present any suitable type ofsupplemental content to a user when the supplemental content is detectedto be relevant to primary content that the user is experiencing.

Additionally, in examples below, processes and acts are described asbeing implemented by various facilities that may be implementedtogether, in a single package. As discussed further below, however,these facilities are merely exemplary of the types of facilities thatmay be implemented and, additionally, these facilities may beimplemented in one or more packages in any suitable combination ordivision.

FIG. 1 illustrates one process for selecting social media data to bepresented to a user based on analyzing a web page to determine contentthat may be relevant to the user. The process 100 of FIG. 1 is merelyexemplary of types of processes that may be implemented, and embodimentsare not limited to implementing the process 100 of FIG. 1.

In one embodiment in which the process 100 may be implemented, a userhas installed a software package that includes a supplemental contentretrieval facility, an entity identification facility, an entitymatching facility, and a display facility. The user may thenconfigure—as discussed in greater detail in connection with FIG. 5below—the supplemental content retrieval facility to retrieve socialmedia data from one or more sources, including from social networkservices with which the user is affiliated. When the user accesses a webpage using a web browser, other facilities may carry out the process 100to detect which units of social media data, retrieved from the sources,should be presented to the user as relevant social media data.

Process 100 begins in block 102, in which an entity identificationfacility identifies entities related to the web page that the user hasaccessed. As discussed above, analyzing a web page to determine thetopic of the web page may be useful in determining current interests ofthe user and thus in determining which social media data may be relevantto the user at that time. To analyze the web page to determine topics,one or more entities that are related to the web page are identified byexamining the text of the web page. Entities in the web page may includeany nouns referenced by the web page. For example, if the user isviewing a web page that includes a news article, the news article mayinclude the words “Washington” and “Obama.” Through the identificationof block 102, the entities “Washington, D.C.” and “President BarackObama” may be identified as related to the web page. Exemplarytechniques for identifying entities related to a web page are discussedin greater detail below in connection with FIGS. 2, 3A, and 3B.

In block 104, for each unit of social media data (e.g., each message inthe social media data) retrieved by the social media data retrievalfacility, an identification of entities related to the unit is carriedout. Similar to the entities identified in block 102, the entitiesidentified in block 104 may be any nouns referenced by the unit ofsocial media data. For example, if the unit includes the words “DC” and“Obama,” the entities “Washington, D.C.” and “President Barack Obama”may be identified as related to the unit of social media data. Exemplarytechniques for identifying entities related to a web page are discussedin greater detail below in connection with FIG. 4.

The process 100 is carried out to detect social media data that may berelevant to the user, based on the web page that the user is viewing.Accordingly, in block 106, a comparison of entities in the web page andentities in the social media data is carried out to detect theintersection of the sets of entities of the web page and the socialmedia data (e.g., to detect matches in the entities). A match inentities may indicate that units of social media are related to the sametopic as the web page and therefore may be relevant to the user.

In some implementations, the comparison of the entities in block 106 mayinclude an exact comparison of the entities related to the web page andthe social media data. In other implementations, the comparison of block106 may additionally or alternatively include a comparison of a categoryof entities, such as a comparison of a type, topic, or class ofentities. Such a categorical comparison may permit more matches to beidentified. For example, while an exact comparison of “Nike” and“Adidas” may not identify a match, some categorical comparisons mayidentify that both are brands of shoes and thus identify a match. Anysuitable comparison technique may be used to identify matches.

In block 108, for each entity that is contained in both the web page andin at least one unit of social media data (i.e., for each entity in theintersection), units of social media data are presented to the user aspotentially-relevant social media data.

The units of social media data may be presented in any suitable manner,including in a user interface that permits the social media data to beviewed by the user together with the web page, without substantiallyobstructing the web page or requiring the user to navigate away from theweb page. For example, the social media data may be presented in asidebar interface, displayed alongside the web page. Presenting socialmedia data in this way allows the social media that is relevant to thesame topic as the web page to be presented to the user at the same timeas the web page, such that the user can experience the web page and thesocial media data together. This type of interface may be useful where,using the example above, the news article of the web page is describingan announcement by the “President Barack Obama” in “Washington, D.C.,”of a new government policy and the social media data includes reactionsof friends of the user to the announcement of that policy. Such apresentation enables the user to quickly and easily determine what therelevant facts are and how people the user trusts are reacting to thosefacts, which allows the user to determine his/her own reaction to thefacts. As another example, if the user is viewing a web page for arestaurant, a similar process may be carried out to present to the usersocial media data relating to the restaurant, including reviews of therestaurant from the user's friends. The user can then view the web pagefor the restaurant (including, for example, the menu) alongside thesocial media data, which may aid the user in choosing whether to eat atthe restaurant.

Though, any suitable interface may be used to present social media datato the user together with the web page in any suitable way. Examples ofuser interfaces that may be implemented in some embodiments arediscussed in greater detail below in connection with FIGS. 7A and 7B.

In this embodiment, the process 100 may be used for each web page todetect current interests of the user from that web page. Accordingly,once the units of social media data that are possibly relevant to theuser's current interests are presented to the user in block 108, theprocess 100 ends.

By identifying social media data that relates to the same topic (basedon a comparison of entities) as a web page that the user is viewing anddisplaying that identified social media data, the user is able to viewrelevant social media data without needing to explicitly sort, filter,or search the social media data. The user can therefore be more certainthat he/she is viewing the social media data that is important to theuser, and may be happy to see the relevant social media data, ratherthan potentially feeling anxious that he/she is missing potentiallyrelevant information.

The processes and actions that are included in the process 100 of FIG. 1can be implemented in any of various ways in different embodiments.Discussed below are examples of ways in which some embodiments mayimplemented these processes and acts. Embodiments are not, however,limited to implementing the process 100 according to any of the examplesprovided below.

As discussed above, an entity identification facility may be implementedin some embodiments to analyze a web page (or other type of primarycontent) to identify entities that are related to the web page,including entities referenced by the web page. Identifying entitiesrelated to a web page may be carried out in any suitable manner. U.S.patent application Ser. No. 12/270,974 (“the '974 application”), Ser.No. 12/614,457 (“the '457 application”), Ser. No. 12/633,798 (“the '798application”), and Ser. No. 12/761,666 (“the '666 application”) eachdescribe techniques that may be used to analyze web pages (and othertypes of primary content) and identify entities related to the webpages. In some embodiments, an entity identification facility mayoperate according to any of the techniques described in theseapplications or any combination of the techniques described in theseapplications. Each of the '974 application, the '457 application, the'798 application, and the '666 application is incorporated herein byreference in its entirety, at least for its discussion of identifyingentities related to web pages and other types of primary content. Anyterminology used in both this disclosure and in any of theseapplications should be accorded a meaning most consistent with the textof this disclosure.

FIG. 2 illustrates one process that may be implemented by an entityidentification facility in some embodiments for identifying entitiesrelated to a web page. Embodiments are not limited to implementing theexemplary process 200, as other processes are possible.

Prior to the start of the process 200, a web page has been selected tobe viewed and has been provided to the entity identification facilityfor analysis. The web page may have already been displayed to the useror may be analyzed by the entity identification facility prior todisplay to the user.

Process 200 begins in block 202, in which all entities referenced in theweb page are identified. Entities may be referenced in the text of theweb page, in code of the web page, in metadata of the web page, incontent or metadata of elements of the web page (e.g., a frame of theweb page, a picture of the web page, a ASP control of the web page,plug-in component of the web page like a Flash element, etc.), or in anyother part of the web page.

Any suitable natural language processing, learning, matching, or othertechnique may be used to identify entities to which a web page relates.In some embodiments, entities may be identified in block 102 when theentities satisfy various parameters. For example, some pieces ofmetadata may be preconfigured as entities, such as a source of the webpage (e.g., an organization or server who provided the web page). Asanother example, the web page may be compared to a grammar thatrecognizes phrases in text such that entities may be identified based onthe phrases. One such phrase could be for identifying an author, such asthe phrase “By _,” where the blank could be satisfied by a name of anauthor that would be identified as an entity. Similarly, a hyperlink toan email address may satisfy a grammar phrase that results in the emailaddress of the hyperlink being identified as an entity. As anotherexample, formatting of words or phrases may be used to identifyentities, such as when the first letter of a word is capitalized or whenthe word is bolded.

In some embodiments, an external source of entities may be additionallyor alternatively used to identify entities in a web page by comparingwords or phrases in the web page to words or phrases in the externalsource. The external source may be any suitable listing of words andphrases, including an external dictionary and/or external encyclopedia.In some implementations, the external source may be an encyclopediaavailable online, such as the Wikipedia® online encyclopedia. In somesuch implementations, the listing of entities maintained by Wikipedia®may be downloaded once or periodically and may available to the entityidentification facility. The entity identification facility may thenidentify entities in the web page by comparing the text of the web pageto the entities in the Wikipedia® listing.

In block 204, regardless of how the entities are identified in block202, the entities may be canonicalized to yield a set of root entities.This may be done so that a comparison of entities is done using commonterms and will produce correct and complete results and not fail tomatch two entities because of a slight change in terminology.Canonicalization may comprise identifying a proper name for an entityfor which a partial name was given (e.g., “Obama” or “Barack HusseinObama” to “President Barack Obama”), determining synonyms or categories(e.g., “tennis shoe” to “athletic shoe”), or determining a root word ofan entity referenced in the web page (e.g., “apples” to “apple”).Canonicalization may be carried out in any suitable manner. In somecases, canonicalization may include using a dictionary having linksbetween terms that identify a root term. In the Wikipedia® listing, forexample, entities are recognized by multiple different words/phrases butlink to one root term that has the definition for the entity. The wordsor phrases that “redirect” to the one root term may therefore becanonicalized, when they are recognized in the web page, to the rootterm.

In some cases, canonicalization may include identifying a correct entityfor a term before canonicalization can be carried out. This may be thecase, for example, with the term “Washington” that could becanonicalized to “George Washington” or “Washington, D.C.” Additionally,in some cases a canonicalized term may be associated with multipleentities. This may be the case, for example, with the term “Boston,”which may refer to one of multiple different cities and towns, a musicalgroup, a genre of dance, or other entities. To determine a correct topicfor a web page, a correct entity should be determined from among the setof possible entities. If an incorrect meaning for an entity wasdetected, or if no meaning was determined for ambiguous entities, theninterests of the user may not be properly determined and irrelevantsocial media data may be presented to a user.

Identifying entities may therefore include, as a part ofcanonicalization and/or as a separate act, determining a context for anentity so as to determine a correct entity. FIG. 2 illustrates thecontextualizing of entities as a separate act, comprising blocks 206 and208. For ease of illustration and description below, the blocks 206 and208 are shown and described as single blocks without a loop, but in someembodiments the process 200 may iterate through each of the entitiescanonicalized in block 204 to determine whether the canonicalizedentities should be contextualized.

In block 206, a canonicalized entity from block 204 is examined todetermine whether the entity has multiple meanings. The review of block206 may be carried out in any suitable manner, such as by examining alisting of entities. In some embodiments, including some embodimentsthat use a Wikipedia® listing of entities to identify entities and/or tocanonicalize entities, the Wikipedia® listing may be consulted todetermine whether an entity is flagged as ambiguous or includes multipledifferent definitions. If no entities have multiple meanings, then theprocess 200 continues to block 210.

However, if a canonicalized entity from block 204 is determined to havemultiple meanings, then a context of the entity may be reviewed todetermine a correct meaning for the entity. In block 208, other entitiesidentified in the web page are reviewed to determine a context for theambiguous entity, which may be used to determine a correct meaning forthe entity. The other entities may be analyzed to determine whether andhow they relate to the ambiguous entity, which may be helpful indetermining a correct meaning of the ambiguous entity. In someembodiments that use the Wikipedia® listing of entities, connectionsbetween definitions of entities, or entities used in the definition ofeach meaning of the ambiguous entity, may be used to determine thecorrect meaning of the entity. For example, if a web page includes manyof the other entities that are used in first definition for an ambiguousentity and does not include many of the other entities that are used ina second definition for the ambiguous entity, the web page is morelikely to relate to the first definition than the second definition. Inreviewing the other entities in block 208, any suitable technique may beused to track the other entities. For example, for each meaning, a countof other entities that are used in defining the meaning and that appearin the web page may be maintained, and the meaning with the highestcount may be selected as the correct meaning. As another example, ifcertain other entities are known to be strongly related to a particularmeaning, and those certain entities appear in the web page, more weightmay be accorded to that particular meaning.

In block 210, once entities identified in the web page are canonicalizedand contextualized, the entities may be ordered according to theirsignificance in the web page. Ordering the entities according tosignificance may be helpful in determining a topic of the web page (andthus the user's current interests) by identifying the most significantentities on the web page, which may be more useful in identifying thetopic than the least significant entities. A significance of each entitymay be determined in any suitable manner based on any suitablemetric(s), examples of which are discussed in greater detail below inconnection with FIGS. 3A and 3B. In some embodiments, a frequency of useof each entity may be tracked and used to identify the most significantentities, with the entities used more frequently being identified as themore significant entities. In some embodiments, a formatting of entitiesmay be tracked and used to identify the most significant entities, withentities appearing in larger font or bold (for example) being identifiedas the more significant entities. In some embodiments, the web page mayexplicitly declare which entities in the page are important orsignificant using, for example, HTML metadata markup, and the markup maybe used in block 210 to identify significant entities and order theentities. Any suitable metric(s) may be used to track a significance ofentities.

In some implementations, ordering entities according to significance mayalso include excluding some entities that are determined not to besignificant. For example, a threshold of significance based on anysuitable metric may be used to determine whether an entity is importantand may be related to the topic of the web page or the user's interestsor whether the entity is merely mentioned in the web page and is notnoteworthy. Because entities are being used to determine a user'sinterests and then present social media data based on those interests,entities that are likely to be dismissed by the user as irrelevant maytherefore be excluded during the ordering of block 210.

Once the entities are ordered in block 210, the process 200 ends. Theentities identified using the process 200 may then be used in anysuitable manner, including in selecting social media data to bepresented to the user.

As discussed above, the process 200, or any other process foridentifying entities, may be implemented by an entity identificationfacility. Embodiments may implement an entity identification facility inany suitable manner on any suitable computing device, as embodiments arenot limited in this respect. In some embodiments, such an entityidentification facility may be implemented on a same computing device asthe web page is being viewed. In other embodiments, however, because ofprocessing load, user choice, or other considerations, the entityidentification facility may be implemented remote from the computingdevice on which the web page is being viewed. In such embodiments, afirst computing device on which the web page is being viewed maytransmit the web page or some identifier for a web page (e.g., a URL) toa second computing device implementing the entity identificationfacility and receive in response a set of entities related to the webpage. The second computing device may implement the entityidentification facility and may analyze the web page upon receiving theweb page or the identifier from the first computing device. In someimplementations that use a second computing device, the second computingdevice may maintain a cache or index of web pages previously or recentlyanalyzed, and may retrieve a previously-identified set of entities for aweb page when a web page is detected to have been previously or recentlyanalyzed. It should be appreciated, though, that embodiments are notlimited to dividing processing between computing devices in anyparticular manner, or dividing processing between computing devices atall.

FIGS. 3A and 3B illustrate two exemplary techniques for determining asignificance of entities in a web page, based on exemplary metrics. Asdiscussed above, any suitable metric or combination of metrics may beused in different embodiments. Accordingly, embodiments are not limitedto implementing either of the processes illustrated in FIGS. 3A and 3B,as others are possible. Further, while the processes of FIGS. 3A and 3Bare illustrated as separate processes, some embodiments may implementboth processes.

Prior to the start of both processes 300 and 330 of FIGS. 3A and 3B,entities have been identified in web pages and, in some embodiments, mayhave been canonicalized and/or contextualized. In some, but not all,embodiments, the identification, canonicalization, and contextualizationmay be carried out as discussed above in connection with blocks 202-208of FIG. 2.

Process 300 of FIG. 3A begins in block 302, in which a loop is startedthat examines each entity detected in a web page. Within the loop,various characteristics of the entity and the web page are examined todetermine a significance of the entity in the web page.

In block 304, a frequency of use of the entity in the web page isdetermined. The frequency of use may be determined by counting a numberof times the entity is used in the web page. The number of times theentity is used may be a count of the number of times the canonicalizedentity is used, such that the count may include the number of times eachof multiple different forms of the entity are used in the web page. Insome cases, one or more thresholds may be used in connection with afrequency of use of entities, such that entities that surpass thethresholds in frequency (e.g., used more than 5 times, or used more than20 times in the web page) are accorded extra significance. For example,once the frequency of such items exceeds the threshold, extra uses maybe added to the frequency score, to weight the entity further. As analternative, a frequency of use may be compared to one or morethresholds to determine a frequency score for the entity, such that anentity used fewer than a threshold number of times is assigned onefrequency score (e.g., 0.4) and an entity used more than a thresholdnumber of times is assigned a second, higher frequency score (e.g.,0.9).

In block 306, a placement of the entity of the web page may beidentified, which may include a placement for each use of the entity inthe web page. The placement of the entity may be used to determinesignificance based on inferring that more significant entities mayappear at a top of a web page and less significant entities may occur ata bottom of a web page. For entities that are used multiple times,identifying a placement of an entity in block 306 may compriseidentifying a topmost placement of the entity, a bottommost placement,an average placement based on examining all placements, or any othersuitable placement. In some embodiments, a placement score may becalculated for an entity from each placement, based on scores/weightsassociated with placements in web pages. A summing, multiplying or othermathematical operation may be carried out based on scores/weights todetermine an overall placement score for an entity.

In block 308, a format of an entity in the web page may be identified,which may include a format for each use of the entity in the web page.The format of the entity may be used to determine significance based oninferring that more significant entities may appear in larger font(e.g., as titles or headers) or in bold, or similar emphasized in someway using formatting. For entities that are used multiple times,identifying a format of an entity in block 306 may comprise identifyinga most emphasized formatting, a least emphasized formatting, anaveraging formatting, or another other formatting, based on informationor metrics relating emphasis to formatting. In some embodiments, aformatting score may be calculated for an entity from each formatting,based on scores/weights associated with formatting in a web page. Asumming, multiplying, or other mathematical operation may be carried outbased on scores/weights to determine an overall formatting score for anentity.

In block 310, based on the frequency, placement, and format of theentity, an overall significance score for the entity is calculated. Theoverall score may be calculated in any suitable manner. In someembodiments, the overall score may be based on an analysis of results ofthe identifications of block 304, 306, and 308. In other embodiments,the overall score may be calculated by performing a calculation onnumerical results of the identifications of block 304, 306, and 308.

In block 312, a determination is made whether more entities remain to beanalyzed. If so, process 300 returns to block 302 to select anotherentity and analyze the significance of that entity. If there are no moreentities to consider, then in block 314 the entities are orderedaccording to the overall significance scores calculated for each inblock 310. As discussed above, in some implementations, the ordering ofblock 314 may include eliminating some entities that are determined notto be significant, such as entities with an overall significance scorebelow a specified threshold.

Once the entities are ordered in block 314, the process 300 ends.

FIG. 3B shows another exemplary process 330 that may be additionally oralternatively used in some embodiments to identify a significance ofentities related to a web page. As discussed further below, process 330determines a significance of an entity based on the entity'srelationship to the structure of a web page.

In block 308 of FIG. 3A, a placement of an entity in a web page was usedto determine a significance of the entity. However, it should beappreciated that some entities that occur near a top of a web page maynot be significant to the content of the web page. This may particularlybe the case if the position of the entity in the code of the web page(e.g., the HyperText Markup Language (HTML) code of the web page), whichmay not directly correspond to the position of the entity in the webpage as rendered. This may be because navigation menu items that offerlinks to other web pages may appear near the top of a web page or nearthe top of code of a web page, but the navigation menu items may beunrelated to the content of the web page. Rather, the navigation menuitems could be static and unchanging elements between different webpages of a web site, as part of a template for the web site. Becausesuch navigation menu items are static and unchanging, they may notdirectly relate to the content of the web page being viewed, and thusmay not be relevant to the user and not relevant in selecting relevantsocial media data to present to the user. According any significance tostatic and unchanging navigation menu items may be imprudent, andaccording special significance to them for appearing near the top of theweb page may be further imprudent.

Analyzing a structure of a web page may therefore aid in identifying themain content of the web page and in identifying sections of the web pagethat are not main content, which may be used to determine a significanceof the entity in the web page.

The process 330 begins in block 332, in which the web page is analyzedand a structure of the web page identified. Identifying the structure ofthe web page may comprise identifying different sections of a web pagebased on an analysis of the code of the web page. For example, if thecode indicates that a particular section of code and content is merely alist of words/phrases, those word phrases may be inferred to be anavigation menu or other list that is irrelevant to the main content ofthe page. In an HTML web page, such a list may be identified by lookingfor repeated “div” tags that have the same style and similarcomposition. Such a technique may also be useful in identifying longersets of content as irrelevant to the content of the page, such as wherea web page that includes a blog may include a list of “comments” on theblog entry. When the code is examined, a lengthy set of text that doesnot include many commands may be identified as the main content of theweb page, which may be considered to be more significant than othercontent. In some embodiments and for some web pages, an analysis of thestructure may identify other files that may also be considered, such asstyle sheets. The style sheets may identify a formatting for text of theweb page and may thus be useful in identifying a significance of someentities. Additionally or alternatively, examining a structure of a webpage may lead to a determination of how the web page will appear whenrendered, which may allow for a better determination of a relevance ofan entity from a placement of an entity in the rendered web page. Theanalysis of the structure of the web page may be carried out in anysuitable manner.

Regardless of the manner in which the web page is analyzed to determinethe structure, in block 334 a loop is started that examines each entityidentified as related to the web page. In block 336, a significance ofan entity is identified based on the structure. The significance may bedetermined from the structure in any suitable manner. For example,entities that are used in a title of the web page (e.g., the metadatatitle or a title as appears in a rendered page) may be rated as moresignificant than entities that are not in a title. As another example,entities that appear within a block of text identified as the maincontent may be determined to be more significant than entities thatappear outside of that block. As another example, entities that appearin a list, such as a list of navigation menu items or a list ofcomments, may be determined to be less relevant than other entities. Asanother example, entities that are determined to appear near a top of arendered page may be identified as more significant than entities thatappear near a bottom of a rendered page.

In block 338, a score is calculated for the entity based on thesignificance of the entity determined in block 336. For entities thatappear multiple times, the score may be calculated based on a highestsignificance, a lowest significance, an average significance, or anyother measure of significance.

In block 340, a determination is made whether more entities remain to beanalyzed. If so, process 330 returns to block 334 to select anotherentity and analyze the significance of that entity. If there are no moreentities to consider, then in block 342 the entities are orderedaccording to the overall significance scores calculated for each inblock 338. As discussed above, in some implementations, the ordering ofblock 342 may include eliminating some entities that are determined notto be significant, such as entities with an overall significance scorebelow a specified threshold.

Once the entities are ordered in block 342, the process 330 ends.

Embodiments are not limited to considering only frequency, placement,formatting, and structure in determining significance. Rather, anysuitable metric may be used. In some embodiments, information notdirectly related to an entity may be used in block 310 to calculate asignificance score for an entity. For example, interests of a user maybe used in calculating a significance score for an entity, includinggeneral interests of a user and specific interests of a user detectedfrom the primary content (e.g., the web page). If a user is detected togenerally be interested in a particular topic, then entities related tothat particular topic may be treated differently from other entities. Insome embodiments, for example, entities related to a user's generalinterests may be considered to be more significant than entities relatedto a user's current interests that may be only passing interests. Thismay be done because the user's general interests may be more relevant tothe user than merely the user's passing interests. As another example,when entities are broadly related to a user's general interest, theentities may be determined to be less significant than entitiesspecifically related to a user's general interest. This may be because auser may be presumed to be familiar with the area of user's generalinterest and so may already be familiar with entities or topics that areonly broadly or generally related to the interest, but may not befamiliar with a specific topic or specific piece of information.Information with which the user is not familiar may be more interestingto the user and therefore may be more significant. Similarly, if a useris an expert in a particular area, the user may not be interested inentities or topics that are simple or intended for novices in thatparticular area, as the user may already be familiar with those entitiesor topics or may find those entities or topics boring. Conversely, if auser is a beginner in a particular area, the user may not be interestedin entities or topics that are intended for experts, because the usermay not be able to understand the entities or topics. Accordingly, adegree of interest or a degree of skill in a topic may be used todetermine a significance of entities related to the topic.

In embodiments where information about a user's general interests orskills is used in calculating a significance score of an entity, anysuitable techniques may be used to determine a user's interests orskills. For example, a user may explicitly provide input to a systemregarding the user's interests or skills. As another example, the usermay consent to his/her interests or skills being monitored or tracked,after which a user's general interests may be inferred from detectinginterests reappearing over time in web pages (or other primary content)accessed by the user.

A skill level of the user in a particular area may be inferred over timefrom comparing entities used in web pages (or other primary content) toknown resources regarding a topic, including beginner texts and advancedtext about the topic, to determine whether web pages more closer relateto the beginner texts or to the advanced texts. Any suitable textualanalysis technique may be used to perform such a comparison. A skilllevel of an entity appearing in a web page may be resolved and used inblock 310 to calculate a significance in a similar manner, by comparingthe entity (and/or other entities related to the web page) to beginnerand/or advanced texts or other resources regarding a topic.

Additionally or alternatively, in some embodiments information aboutsocial media data to which entities relate may be used to determine asignificance of the entities in block 310. For example, when an entityrelated to a web page appears in a larger number of units of socialmedia data, the entity may be determined to be more significant than anentity appearing in a smaller number of units of social media data. Asanother example, sources of units social media data to which an entityrelates may be considered. Units of social media data originating from aparticular social network service may be considered more significantthan units of social media data originating from other social networkservices. Significance of social network services may be determined inany suitable manner, including based on user input or based on detectionof user interactions with units of social media data originating fromsocial network services. Accordingly, entities appearing in the web pagethat also appear in social media data originating from significantsources may be determined to be more significant than other entitiesappearing in the web page. Additionally or alternatively, a user of asocial network service that created a unit of social media data may beconsidered in determining a significance of an entity based on a sourceof social media data relating to the entity. Units originating fromother users to whom the user has strong connection or whom the usertrusts may be more significant than other units of social media data. Adegree of closeness or trust between users may be determined in anysuitable manner, including by determining an amount of interactionbetween the users in the social network service or based on explicituser input. Accordingly, entities appearing in the web page that alsoappear in social media data originating from these close or trustedusers may be determined to be more significant than other entitiesappearing in the web page.

Identifying entities related to a web page and their significance may beuseful, in embodiments, for selecting units of social media data to bepresented to the user as potentially-relevant social media data. Theunits of social media data that are presented are those that may relateto the same topic as the web page that the user is currently viewing.

Accordingly, in some embodiments, units of social media data may beanalyzed to determine entities to which each unit relates, such thatunits of social media data that relate to the same entities as the webpage may be presented to the user. Embodiments may examine social mediadata in any suitable manner to identify entities to which units ofsocial media data relate. In some implementations, an entityidentification facility that is used to identify entities related to webpages may also be used to identify entities related to units of socialmedia data. In other implementations, however, a different entityidentification facility using a different entity identificationtechnique may be used. A different facility and different technique maybe used, for example, where a technique used for a large amounts of textin a web page is not well adapted for use with small amounts of text insocial media data, and a different technique that is well-adapted forsocial media data may be used instead.

Identifying entities related to units of social media data may becarried out in any suitable manner. The '974 application, the '457application, the '798 application, and the '666 application (referencedabove) each describe techniques that may be used to analyze social mediadata (and other types of supplemental content) and identify entitiesrelated to the social media data. In some embodiments, an entityidentification facility may operate according to any of the techniquesdescribed in these applications or any combination of the techniquesdescribed in these applications to identify entities related to socialmedia data. Each of the '974 application, the '457 application, the '798application, and the '666 application is incorporated herein byreference in its entirety, at least for its discussion of identifyingentities related to social media data and other types of supplementalcontent. Any terminology used in both this application and in any ofthese applications should be accorded a meaning most consistent with thetext of this application.

FIG. 4 illustrates one process that may be implemented by an entityidentification facility in some embodiments for identifying entitiesrelated to social media data. Embodiments are not limited toimplementing the exemplary process 400, as other processes are possible.

Prior to the start of the process 400, social media data may have beenmade available to the entity identification facility, including by beingreceived at a computing device implementing the entity identificationfacility. The social media data may originate from any suitable sourceand be received in any suitable manner, examples of which are discussedin greater detail below in connection with FIG. 5.

Process 400 begins in block 402, in which a loop begins that examineseach unit of social media data available to the entity identificationfacility.

In block 404, all entities referenced in the unit of social media dataare identified. The entities may be identified in any suitable manner,including according to exemplary techniques described above inconnection with block 202 of FIG. 2. For example, a listing of entitiesavailable from an external source (e.g., Wikipedia®) may be used toidentify entities. Entities may be identified in any suitable part of aunit social media data, including both in the data of a unit and inmetadata of a unit. For example, a word or phrase used in a unit (e.g.,a word or phrase used in a message or in a caption of a picture orvideo) may be identified as an entity, a person creating a unit may beidentified as an entity, a location at which a unit was created (e.g., ageographic location) may be identified as an entity, or any othersuitable information contained within a unit of social media data may beidentified as an entity.

In block 406, the entities identified in block 404 are canonicalized.The canonicalization may be carried out in any suitable manner,including according to exemplary techniques described above inconnection with block 204 of FIG. 2. For example, the listing ofentities available from the external source may be used to identify aroot entity for each entity identified in block 404.

As discussed above in connection with FIG. 2, canonicalization mayinclude identifying a correct entity for a term before canonicalizationcan be carried out. This may be the case, for example, with the term“Washington” that could be canonicalized to “George Washington” or“Washington, D.C.” Additionally, in some cases a canonicalized term maybe associated with multiple entities. This may be the case, for example,with the term “Boston,” which may refer to one of multiple differentcities and towns, a musical group, a genre of dance, or other entities.To properly relate social media data to a user interests (based on theanalysis of the web page, as discussed above), a correct entity shouldbe determined from among the set of possible entities. If an incorrectmeaning for an entity was detected, or if no meaning was determined forambiguous entities, then interests of the user may not be properlydetermined and irrelevant social media data may be presented to a user.

Identifying entities may therefore include, as a part ofcanonicalization and/or as a separate act, determining a context for anentity so as to determine a correct entity. FIG. 4 illustrates thecontextualizing of entities as a separate act, comprising blocks 408 and410. For ease of illustration and description below, the blocks 408 and410 are shown and described as single blocks without a loop, but in someembodiments the process 400 may iterate through each of the entitiescanonicalized in block 406 to determine whether the canonicalizedentities should be contextualized.

In block 408, a canonicalized entity from block 406 is examined todetermine whether the entity has multiple meanings. The review of block408 may be carried out in any suitable manner, such as by examining alisting of entities. In some embodiments, including some embodimentsthat use a Wikipedia® listing of entities to identify entities and/or tocanonicalize entities, the Wikipedia® listing may be consulted todetermine whether an entity is flagged as ambiguous or includes multipledifferent definitions. If no entities have multiple meanings, then theprocess 400 continues to block 412.

However, if a canonicalized entity from block 406 is determined to havemultiple meanings, then a context of the entity may be reviewed todetermine a correct meaning for the entity. In block 410, other entitiesidentified in the unit of social media data may be reviewed to determinea context for the ambiguous entity, which may be used to determine acorrect meaning for the entity. This review of other entities may becarried out in any suitable manner, including according to techniquesdescribed above in connection with block 208 of FIG. 2. In someembodiments, a unit of social media data may be too short to includeother entities, or may not include enough other entities for an entityto be disambiguated. In such cases, other units of social media datathat are related in some way to the unit may be analyzed. For example,other units of social media data that were created by a same person(e.g., other messages from another user of a social network service) maybe analyzed to determine other entities referenced in those units ofsocial media data. As another example, units of social media datagenerated by others but that are related in some way to the unit (e.g.,responses to messages, or comments on pictures) may be analyzed todetermine other entities referenced in those units. As another example,other units of social media data that include the currently-selectedentity may be analyzed to determine other entities referenced in thoseunits. The other units of social media data may be helpful, as unitsgenerated by a same person, units generated as responses or that areotherwise related, or units that were generated by another person withina same group of people may be likely to be related to the same topic.Accordingly, the other entities referenced by these other units ofsocial media data may be helpful in determining a correct meaning for anambiguous entity related to the currently-selected unit of social mediadata. These other entities may be used in any suitable manner, includingaccording to techniques described above in connection with block 208 ofFIG. 2.

Once all entities of a unit are identified, canonicalized, andcontextualized, in block 412 a determination is made regarding whetheradditional units of social media data are available for analysis. If so,the process 400 returns to block 402 to select and analyze another unitof social media data. If no more units exist, however, the process 400ends.

After the process 400 ends, units of social media data and the entitiesidentified as related to those units may be used in any suitable manner.In some embodiments, an index may be created for the social media databased on entities, that indexes units of social media data based on theentities to which they relate. Additionally, the set of social mediadata may be queried, as discussed in greater detail below in connectionwith FIG. 6, to determine units of social media data that relate toparticular entities and that may be presented to the user as relevant tothe user at that time.

As discussed above, social media data may be received from any suitablesource. FIG. 5 shows one process that may be carried out in someembodiments for identifying sources of social media data and retrievingsocial media data from the sources. Embodiments are not limited toimplementing the process 500 of FIG. 5, however, as other processes arepossible.

Process 500 begins in block 502, in which user input is received thatspecifies at least one source of social media data that the user wouldlike to have processed using techniques described herein to determinerelevant social media data. The user may specify any suitable source ofsocial media data in any suitable manner. For example, the user mayspecify particular social network service with which the user has anaccount (e.g., Facebook®, Twitter®, and others). In some embodiments, asupplemental content retrieval facility may be preconfigured regardinghow to contact some popular social network services to retrieve data,including the particular locations from which to retrieve data and theparticular protocols to communicate to the services using. In otherembodiments, a user may specify locations from which to retrieve dataand protocols to use.

In block 504, a loop is started for each source of social media datathat the user identified in block 504. In block 506, for a particularsource of social media data, any necessary user credentials may bereceived for the source (e.g., user and password) and the credentialsare used, where necessary, to log in to the service. In block 508, atleast one unit of social media data is retrieved from the source by asupplemental content retrieval facility and stored by the facility to beprocessed in any suitable manner.

In block 510, a determination is made regarding whether any othersources of social media data remain to be processed. If so, then process500 returns to block 504 to select another source of social media data.If not, then process 500 continues to block 512 where the retrievedunits of social media data are made available to be analyzed todetermine entities to which each unit relates. Once the entities havebeen examined in block 512, the process 500 ends.

As discussed above, the process 400, or any other process foridentifying entities, may be implemented in an entity identificationfacility. Embodiments may implement an entity identification facility inany suitable manner on any suitable computing device, as embodiments arenot limited in this respect. In some embodiments, such an entityidentification facility for the social media data may be implemented ona same computing device as the web page is being viewed. In otherembodiments, however, because of processing load, user choice, or otherconsiderations, the entity identification facility may be implementedremote from the computing device on which the web page is being viewed.

Accordingly, a supplemental content retrieval facility may be similarlyimplemented on a same computing device as the web page is being viewedor on a separate computing device. Implementing the supplemental contentretrieval facility may be important in some embodiments for privacyconsiderations. Implementing the supplemental content retrieval facilityon the same computing device as the web page is being viewed may assistin ensuring the confidentiality or privacy of social media data, as thesocial media data is only retrieved by a computing device that the useris operating. In other embodiments, though, for the reasons listedabove, a supplemental content retrieval facility and an entityidentification facility may be implemented on a remote computing device.In such embodiments, a first computing device on which the web page isbeing viewed may receive the user specifications of the social mediadata to be analyzed and transmit the specifications to a secondcomputing device implementing the supplemental content retrievalfacility. The second computing device may then retrieve the social mediadata and carry out the entity identification using the entityidentification facility, and then provide the social media data and theentities to the first computing device. Though, embodiments are notlimited to dividing processing between computing devices in anyparticular manner, or dividing processing between computing devices atall.

As discussed above, techniques described herein are useful fordetermining relevant social media data to be presented to a user basedon an analysis of topics that are currently relevant to a user. Topicscurrently relevant to a user may be determined based on anidentification of entities related to a web page (or other type ofprimary content) that a user is currently viewing. Social media data maybe determined to be relevant to a user when the social media datarelates to the same entities as the web page, as the social media datamay be inferred to relate to the same topics and be relevant to the userfor that reason.

FIG. 6 shows one exemplary process that may be used for selecting socialmedia data to be presented to a user based on entities identified inboth a web page and in social media data. Embodiments are not limited toimplementing the process 600 of FIG. 6, as others are possible.

Process 600 begins in block 602, in which user input regarding a desirednumber of matches is received. The user input may specify a specificnumber of desired matches or may specify a range or relative amount(e.g., “more” or “less”), or may specify a desired number in any othermanner. The user input of block 602 may aid the user in not feelingoverwhelmed by an amount of social media data presented to the user orin identifying the most relevant social media data to be viewed.

In block 604, a number of entities that are related to the web page andto at least one unit of social media data are selected. Selectingentities that relate to both the web page and the at least one unit ofsocial media data ensures that entities for which social media data isactually available are presented to a user and that a satisfactorynumber of entities are presented. The entities selected may be the topentities in an ordering of entities according to significance in the webpage or may be any other set of entities.

The entities that relate to both the web page and at least one unit ofsocial media data may be identified in any suitable manner. In someembodiments that maintain an index of entities related to units ofsocial media data, an entity matching facility may, for each entityidentified as related to the web page, query an index for the units ofsocial media data to determine whether the index includes the entityand/or the entity is related, according to the index, to at least oneunit of social media data.

In block 606, for each of the entities selected in block 604, a numberof units of social media data that relate to that entity are selected.The units of social media data may be selected in any suitable manner.For example, units created most recently may be selected first and unitscreated farthest in the past may be selected last. As another example,units that relate to multiple entities selected in block 604 may beselected first, as these units may be inferred to be the most relevantto the user. As another example, previous user interaction with socialmedia data presented in this way may be used, such as where a userindicated a response to units from certain people or units with othercertain characteristics. Any suitable metrics may be used in anysuitable manner to select units of social media data in block 606, asembodiments are not limited in this respect.

The units of social media data may be selected in any suitable manner.In some embodiments, any suitable query may be conducted of a data storethat includes the units of social media data, including a query of oneor more indices maintained by the social media data or a query of theunits of social media data themselves that may be carried out as ananalysis of the social media data. The manner of selecting the units inblock 606 may vary based on how the units of social media data arestored (e.g., as individual units stored separately in a storage medium,as data in a markup file, as data in a relational database, etc.).

In block 608, the units of social media data selected in block 606 arepresented to the user categorized by an entity to which they relate inany suitable interface. Where units of social media data relate tomultiple entities, the entity may be categorized multiple times, by eachentity, or any suitable factors may be used to determine an entity bywhich to categorize a unit. For example, units may be categorized by themost significant entity (e.g., most significant in the web page) towhich they relate. As another example, units may be categorizedaccording to the entity used most frequently or used first in the unitof social media data. Any suitable factors may be used to categorizeunits by entity, as embodiments are not limited in this respect.

The units of social media data may be presented to the user in anysuitable interface, including as a sidebar that enables the social mediadata to be displayed together with the web page. In some embodiments,the user interface that displays the social media data may be a part ofthe web browser a user is using to view a web page.

FIGS. 7A and 7B show examples of user interfaces that may be used insome embodiments in block 608 to present social media data to a usertogether with a web page. The user interfaces illustrated in FIGS. 7Aand 7B may be used in embodiments that implement the user interface as apart of a web browser. Embodiments that implement the user interfaceoutside of the web browser (or any other application for experiencingprimary content) may use any other suitable user interface.

The user interface 700 includes two regions 702 and 704. Region 702displays the web page that the user is currently viewing while region704 displays a list of social media data that may be relevant to theuser. The list of social media data is shown in the region 704 ascategorized based on entity, with each category expandable to see theunits of social media data related to that entity. The category“Category 6” is shown expanded in the region 704, with four units ofsocial media content also shown under that category. For each unit, auser that created the unit is identified and a picture is shown, inaddition to the content of the unit.

In an exemplary embodiment, the web page shown in the region 702includes an article about a professional athlete (e.g., “professionalathlete”), who was detected as an entity to which the web page relates.The region 704 includes a category “professional athlete” that, ifexpanded, lists pieces of social media data that also relate to theentity and that have been determined to be potentially relevant to theuser.

FIG. 7B shows another example of a user interface that also includes tworegions: a web page region 712 and a social media data display region714. In an exemplary embodiment, the web page may include an articleabout Chile, and Chile can be identified as an entity to which the webpage relates. The social media data listed in the region 714 thereforeincludes social media data related to the entity Chile, which is shownexpanded in the region 714 with 4 units of social media data shown asblurbs. For each of the blurbs categorized under Chile, a person whocreated the unit of social media data is identified and picture isshown, the time at which the unit was created is identified, and thecontent of the unit of social media data is shown. Additionally, anenlarged view 714A of one particular unit of social media data is shownthat shows the content of the blurb in larger font and may, in somecases, show more of the content that was shown in the blurb. A user mayaccess the enlarged view 714A by clicking on one of the blurbs shown inthe region 714. The enlarged view 714A may additionally provide the userways to interact with the unit of social media content, such as byindicating that the user “likes” the unit (which may create a message tobe shared in a social network service), creating a reply to the unit ofsocial media content, or sharing the unit of social media content withothers by creating a new message including the unit of social mediacontent.

Regardless of the interface used to present units of social mediacontent in block 608, once the units of social media content arepresented in block 608, the process 600 ends.

As discussed above, in various examples described herein primary contentis described as a web page and supplemental content is described associal media data. However, embodiments are not limited to operatingwith these types of primary and supplemental content or any otherparticular types of primary and supplemental content. Rather, asdiscussed above, primary content may include any type of electroniccontent that a user is presently or was recently experiencing, andsupplemental content includes any distributed information to which theuser has subscribed. Other examples of primary content include wordprocessing documents, emails, instant messages, photographs, and videos.Other examples of supplemental content include entries in an RSS feed(e.g., blog entries), emails, instant messages, and photographs.

Techniques operating according to the principles described herein may beimplemented in any suitable manner. Included in the discussion above area series of flow charts showing the steps and acts of various processesthat select supplemental content to be displayed to a user when thesupplemental content is potentially relevant to the user, based on ananalysis of primary content that the user is experiencing. Theprocessing and decision blocks of the flow charts above represent stepsand acts that may be included in algorithms that carry out these variousprocesses. Algorithms derived from these processes may be implemented assoftware integrated with and directing the operation of one or moremulti-purpose processors, may be implemented as functionally-equivalentcircuits such as a Digital Signal Processing (DSP) circuit or anApplication-Specific Integrated Circuit (ASIC), or may be implemented inany other suitable manner. It should be appreciated that the flow chartsincluded herein do not depict the syntax or operation of any particularcircuit, or of any particular programming language or type ofprogramming language. Rather, the flow charts illustrate the functionalinformation one of ordinary skill in the art may use to fabricatecircuits or to implement computer software algorithms to perform theprocessing of a particular apparatus carrying out the types oftechniques described herein. It should also be appreciated that, unlessotherwise indicated herein, the particular sequence of steps and actsdescribed in each flow chart is merely illustrative of the algorithmsthat may be implemented and can be varied in implementations andembodiments of the principles described herein.

Accordingly, in some embodiments, the techniques described herein may beembodied in computer-executable instructions implemented as software,including as application software, system software, firmware,middleware, or any other suitable type of software. Suchcomputer-executable instructions may be written using any of a number ofsuitable programming languages and/or programming or scripting tools,and also may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

When techniques described herein are embodied as computer-executableinstructions, these computer-executable instructions may be implementedin any suitable manner, including as a number of functional facilities,each providing one or more operations needed to complete execution ofalgorithms operating according to these techniques. A “functionalfacility,” however instantiated, is a structural component of a computersystem that, when integrated with and executed by one or more computers,causes the one or more computers to perform a specific operational role.A functional facility may be a portion of or an entire software element.For example, a functional facility may be implemented as a function of aprocess, or as a discrete process, or as any other suitable unit ofprocessing. If techniques described herein are implemented as multiplefunctional facilities, each functional facility may be implemented inits own way; all need not be implemented the same way. Additionally,these functional facilities may be executed in parallel or serially, asappropriate, and may pass information between one another using a sharedmemory on the computer(s) on which they are executing, using a messagepassing protocol, or in any other suitable way.

Generally, functional facilities include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the functional facilities may be combined or distributed as desiredin the systems in which they operate. In some implementations, one ormore functional facilities carrying out techniques herein may togetherform a complete, stand-alone software package, for example as a softwareprogram application such as Microsoft® Shimmer. These functionalfacilities may, in alternative embodiments, be adapted to interact withother, unrelated functional facilities and/or processes, to implement asoftware program application. In some such alternative embodiments, thefunctional facilities may be implemented as a plug-in, optionalcomponent, or other add-on to an application program. The functionalfacilities may be implemented as an add-on to, for example, a webbrowser such as Microsoft® Internet Explorer® (e.g., the Microsoft®Shimmer plug-in to Internet Explorer®), an e-mail client such asMicrosoft® Outlook®, a word processing client such as Microsoft® Word, aphoto processing application such as Microsoft® Windows® Photo Gallery,or as an add-on to any other suitable application. In otherimplementations, the functional facilities may be adapted to interactwith other functional facilities in such a way as form an operatingsystem, including the Windows® operating system, available from theMicrosoft® Corporation of Redmond, Wash. In other words, in someimplementations, the functional facilities may be implementedalternatively as a portion of or outside of an application program oroperating system.

In some embodiments where the functional facilities are implemented as astand-alone application or as a part of an operating system, thefunctional facilities may detect primary content that the user isexperiencing by monitoring other applications with which the user isinteracting. In some embodiments where the functional facilities areimplemented as an add-on to an application, the functional facilitiesmay detect primary content that the user is experiencing by monitoringthe application to which the facilities are added.

Some exemplary functional facilities have been described herein forcarrying out one or more tasks. It should be appreciated, though, thatthe functional facilities and division of tasks described is merelyillustrative of the type of functional facilities that may implement theexemplary techniques described herein, and that embodiments are notlimited to being implemented in any specific number, division, or typeof functional facilities. In some implementations, all functionality maybe implemented in a single functional facility. It should also beappreciated that, in some implementations, some of the functionalfacilities described herein may be implemented together with orseparately from others (i.e., as a single unit or separate units), orsome of these functional facilities may not be implemented.

Computer-executable instructions implementing the techniques describedherein (when implemented as one or more functional facilities or in anyother manner) may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), a persistent or non-persistent solid-state memory (e.g.,Flash memory, Magnetic RAM, etc.), or any other suitable storage media.Such a computer-readable medium may be implemented in any suitablemanner, including as computer-readable storage media 806 of FIG. 8described below (i.e., as a portion of a computing device 800) or as astand-alone, separate storage medium. As used herein, “computer-readablemedia” (also called “computer-readable storage media”) refers totangible storage media. Tangible storage media are non-transitory andhave at least one physical, structural component. In a“computer-readable medium,” as used herein, at least one physical,structural component has at least one physical property that may bealtered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

In some, but not all, implementations in which the techniques may beembodied as computer-executable instructions, these instructions may beexecuted on one or more suitable computing device(s) operating in anysuitable computer system, including the exemplary computer system ofFIG. 9. Functional facilities that comprise these computer-executableinstructions may be integrated with and direct the operation of a singlemulti-purpose programmable digital computer apparatus, a coordinatedsystem of two or more multi-purpose computer apparatuses sharingprocessing power and jointly carrying out the techniques describedherein, a single computer apparatus or coordinated system of computerapparatuses (co-located or geographically distributed) dedicated toexecuting the techniques described herein, one or moreField-Programmable Gate Arrays (FPGAs) for carrying out the techniquesdescribed herein, or any other suitable system.

FIG. 8 illustrates one exemplary implementation of a computing device inthe form of a computing device 800 that may be used in a systemimplementing the techniques described herein, although others arepossible. It should be appreciated that FIG. 8 is intended neither to bea depiction of necessary components for a computing device to operate inaccordance with the principles described herein, nor a comprehensivedepiction.

Computing device 800 may comprise at least one processor 802, a networkadapter 804, and computer-readable storage media 806. Computing device800 may be, for example, a desktop or laptop personal computer, apersonal digital assistant (PDA), a smart mobile phone, a server, or anyother suitable computing device. Network adapter 804 may be any suitablehardware and/or software to enable the computing device 800 tocommunicate wired and/or wirelessly with any other suitable computingdevice over any suitable communication network. The communicationnetwork may include wireless access points, switches, routers, gateways,and/or other networking equipment as well as any suitable wired and/orwireless communication medium or media for exchanging data between twoor more computers. In some cases, the communication network may includemultiple networks and may include the Internet. Computer-readable media806 may be adapted to store data to be processed and/or instructions tobe executed by processor 802. Processor 802 enables processing of dataand execution of instructions. The data and instructions may be storedon the computer-readable storage media 806 and may, for example, enablecommunication between components of the computing device 800.

The data and instructions stored on computer-readable storage media 806may comprise computer-executable instructions implementing techniqueswhich operate according to the principles described herein. In theexample of FIG. 8, computer-readable storage media 806 storescomputer-executable instructions implementing various facilities andstoring various information as described above. Computer-readablestorage media 806 may store a primary content viewer 808 that maypresent primary content to a user via any suitable user interface and asupplemental content presentation facility 816 that may presentsupplemental content to a user via any suitable user interface.Computer-readable storage media 806 may also store a supplementalcontent retrieval facility 810 for retrieving supplemental content fromone or more sources and a data store 810A of supplemental content. Thecomputer-readable storage media 806 may further store an entityidentification facility 812 that may maintain any suitable data store812A of entities detected in either or both of primary content andsupplemental content. The computer-readable storage media 806 mayfurther store an entity matching facility 814 that may query a datastore of entities related to primary content and to supplemental contentto determine matches between primary content and supplemental contentand to select supplemental content to be presented to the user based onthe matches.

While not illustrated in FIG. 8, a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets. As another example, acomputing device may receive input information through speechrecognition or in other audible format.

Computing device 800 may be implemented in any suitable computer system.FIG. 9 shows one example of a computer system in which some embodimentsmay operate. The computer system 900 includes client computing devices902, including smart phone 902A, personal digital assistant (PDA) 902B,and desktop personal computer 902C. Each of the client computing devices902 may be used by users to experience primary content and may presentsupplemental content to be viewed by the user when the supplementalcontent is detected to be potentially relevant to the user. In someembodiments, each of the client computing devices 902 may analyzeprimary content for entities, retrieve supplemental content from one ormore sources to be analyzed for entities and presented to the user basedon a comparison to entities of the primary content, and present thesupplemental content to the user. For example, each of the clientcomputing devices may retrieve social media data from a server 906 thathosts a social network service and maintains a data store 906A of socialmedia data. In other embodiments, however, all or some of the clientcomputing devices 902A (e.g., smart phone 902A and PDA 902B, withlimited processing capabilities) may provide primary content and/orsupplemental content to a server 904 to be analyzed. The server 904 maystore entities appearing in primary and/or supplemental content in adata store 904A and provide the entities to the client computing devices902.

Client computing devices 902 and servers 904, 906 may be connected viaany suitable communication network 908. In embodiments, communicationnetwork 908 may be any suitable wired and/or wireless communicationnetwork that allows data to be exchanged between the computing devices,including a local area network, an enterprise network, and/or theInternet.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Various aspects of the embodiments described above may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

Having thus described several aspects of at least one embodiment, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe principles described herein. Accordingly, the foregoing descriptionand drawings are by way of example only.

1. A computing system comprising: a processor; and memory that storesinstructions that, when executed by the processor, cause the processorto perform acts comprising: identifying, in primary content presented ona display to a user, an entity that is referenced in the primarycontent; responsive to identifying the entity, determining that a socialmedia message posted by a second user also references the entity,wherein the social media message is posted by the second user by way ofa social media application; and responsive to determining that thesocial media message also references the entity, causing the socialmedia message to be displayed concurrently with the primary content onthe display, wherein the social media message is caused to be displayedon the display based upon a relationship between the user and the seconduser in the social media application.
 2. The computing system of claim1, the acts further comprising: computing a significance score for theentity, wherein the significance score is indicative of a predictedlevel of interest of the entity to the user, and further wherein thesocial media message is caused to be displayed on the display based uponthe significance score for the entity.
 3. The computing system of claim2, the acts further comprising: determining that the significance scoreexceeds a predefined threshold; and causing the social media message tobe displayed on the display only in response to determining that thesignificance score exceeds the predefined threshold.
 4. The computingsystem of claim 2, wherein the significance score is based upon a numberof social media messages that include the entity, wherein the socialmedia messages are posted by contacts of the user by way of the socialmedia application.
 5. The computing system of claim 2, wherein theentity is indicative of a topic of the primary content, and furtherwherein the significance score is based upon input provided by the userthat indicates that the user is interested in the topic.
 6. Thecomputing system of claim 1, wherein the social media message is causedto be displayed on the display based upon a number of electronicinteractions between the user and the second user by way of the socialmedia application.
 7. The computing system of claim 1, wherein thesocial media message is caused to be displayed on the display furtherbased upon a time when the social media message was posted by the seconduser by way of the social media application.
 8. The computing system ofclaim 1, wherein the relationship between the user and the second usercomprises a degree of trust between the user and the second user,wherein the degree of trust is computed based upon one of explicit inputfrom the user or a number of interactions between the user and thesecond user by way of the social media application.
 9. The computingsystem of claim 1, wherein the primary content includes several entitiesthat comprise the entity, the acts further comprising: ranking theseveral entities, wherein the entity is a most highly ranked entity fromamongst the several entities, and further wherein the social mediamessage is caused to be displayed on the display due to the entity beingthe most highly ranked entity from amongst the several entities.
 10. Thecomputing system of claim 1, wherein the primary content references asecond entity, wherein the social media message also references thesecond entity, and further wherein the social media message is caused tobe displayed on the display based upon the social media messagereferencing both the first entity and the second entity.
 11. A methodexecuted by a processor of a computing system, the method comprising:identifying, in primary content presented on a display to a user, anentity that is referenced by the primary content; responsive toidentifying the entity, determining that a social media message postedby a second user also references the entity, wherein the social mediamessage is posted by the second user by way of a social mediaapplication; and responsive to determining that the social media messagealso references the entity, causing the social media message to bedisplayed concurrently with the primary content on the display, whereinthe social media message is caused to be displayed on the display basedupon a relationship between the user and the second user in the socialmedia application.
 12. The method of claim 11, further comprising:computing a significance score for the entity, wherein the significancescore is indicative of a predicted level of interest of the entity tothe user, and further wherein the social media message is caused to bedisplayed on the display based upon the significance score for theentity.
 13. The method of claim 12, further comprising: determining thatthe significance score exceeds a predefined threshold; and causing thesocial media message to be displayed on the display only in response todetermining that the significance score exceeds the predefinedthreshold.
 14. The method of claim 12, wherein the significance score isbased upon a number of social media messages that include the entity,wherein the social media messages are posted by contacts of the user byway of the social media application.
 15. The method of claim 12, whereinthe entity is indicative of a topic of the primary content, and furtherwherein the significance score is based upon input provided by the userthat indicates that the user is interested in the topic.
 16. The methodof claim 11, comprising: causing the social media message to bedisplayed on the display based upon a number of electronic interactionsbetween the user and the second user by way of the social mediaapplication.
 17. The method of claim 11, wherein the relationshipbetween the user and the second user comprises a degree of trust betweenthe user and the second user, the method further comprising: computingthe degree of trust based upon one of explicit input from the user or anumber of interactions between the user and the second user by way ofthe social media application.
 18. A computer-readable storage mediumcomprising instructions that, when executed by a processor, cause theprocessor to perform acts comprising: identifying, in primary contentpresented on a display to a user, an entity that is referenced in theprimary content; responsive to identifying the entity, determining thata social media message posted by a second user also references theentity, wherein the social media message is posted by the second user byway of a social media application; and responsive to determining thatthe social media message also references the entity, causing the socialmedia message to be displayed concurrently with the primary content onthe display, wherein the social media message is caused to be displayedon the display based upon a relationship between the user and the seconduser in the social media application.
 19. The computer-readable storagemedium of claim 18, the acts further comprising: computing asignificance score for the entity, wherein the significance score isindicative of a predicted level of interest of the entity to the user,and further wherein the social media message is caused to be displayedon the display based upon the significance score for the entity.
 20. Thecomputer-readable storage medium of claim 18, wherein the social mediamessage is caused to be displayed on the display based upon a number ofelectronic interactions between the user and the trusted user by way ofthe social media application.